US6751584B2 - Automatic clustering of tokens from a corpus for grammar acquisition - Google Patents

Automatic clustering of tokens from a corpus for grammar acquisition Download PDF

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US6751584B2
US6751584B2 US09/912,461 US91246101A US6751584B2 US 6751584 B2 US6751584 B2 US 6751584B2 US 91246101 A US91246101 A US 91246101A US 6751584 B2 US6751584 B2 US 6751584B2
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corpus
tokens
frequency vectors
clusters
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Srinivas Bangalore
Giuseppe Riccardi
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Nuance Communications Inc
AT&T Properties LLC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/253—Grammatical analysis; Style critique
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/284—Lexical analysis, e.g. tokenisation or collocates

Definitions

  • the present invention relates to an application that builds linguistic models from a corpus of speech.
  • An embodiment of the present invention provides a method of learning grammar from a corpus, in which context words are identified from a corpus. For the other non-context words, the method counts the occurrence of predetermined relationships with the context words, and maps the counted occurrences to a multidimensional frequency space. Clusters are grown from the frequency vectors. The clusters represent classes of words; words in the same cluster posses the same lexical significancy and provide an indicator of grammatical structure.
  • FIG. 1 is a flow diagram of a method of an embodiment of the present invention.
  • FIG. 2 illustrates mapping frequency vectors that may be obtained during operation of the present invention.
  • FIG. 3 illustrates an exemplary cluster tree
  • Embodiments of the present invention provide a system that automatically builds a grammatical model from a corpus of speech.
  • the present invention uses clustering to group words and/or phrases according to their lexical significance. Relationships between high frequency words called “context words” and other input words are identified.
  • the words to be clustered are each represented as a feature vector constructed from the identified relationships. Similarities between two input words are measured in terms of the distance between their feature vectors. Using these distances, input words are clustered according to a hierarchy. The hierarchy is then cut at a certain depth to produce clusters which are then ranked by a “goodness” metric. Those clusters that remain identify words or tokens from the corpus that possess similar grammatical significance.
  • Clustering per se is known. In the context of language modeling, clustering has typically been used on words to induce classes that are then used to predict smoothed probabilities of occurrence for rare or unseen events in the training corpus. Most clustering schemes use the average entropy reduction to decide when two words fall into the same cluster. Prior use of clustering, however, does not provide insight into language model of grammar.
  • FIG. 1 illustrates a method of the present invention according to a first embodiment.
  • the method operates upon input text, a set of words from which the grammar model shall be constructed.
  • the input text comprises a set of single words or phonemes.
  • the method identifies context words (Step 1010 ).
  • Context words are those words or phonemes in the input text that occur with the highest frequency.
  • the method 1000 may cause a predetermined number of words (say, 50) that occur with the highest frequency to be identified as context words.
  • the method 1000 determines relationships that may exist between the context words and the remaining words, called “input words” herein, in the input text. For example, the method 1000 may determine how many times and in which positions an input word appears adjacent to a context word. Table 1 below illustrates relationships that may exist between certain exemplary input words and exemplary context words.
  • f ijk represents, for a given input word i; how many times a context word C; and non-context word i; appears within a predetermined relationship.
  • f 111 -F 1114 each represent the number of time the input word “Chicago” and the context word “to” appear within adjacencies of ⁇ 2 words, ⁇ 1 word, +1 word and +2 words respectively.
  • the method 1000 builds clusters of input words (Step 1030 ).
  • input words having the same lexical significance should possess similar vectors in the frequency space.
  • city names will exhibit frequency characteristics that are similar to each other but different from other input words having a different lexical significance. They will be included in a cluster (say, cluster 10 , FIG. 2 ). So, too, with colors. They will be included in another cluster (say, cluster 20 ). Where words exhibit similar frequency significance, they are included within a single cluster.
  • a cluster may be represented in an N-dimensional frequency space by a centroid coordinate and a radius indicating the volume of the cluster.
  • the radius indicates the “compactness” of the elements within a cluster. Where a cluster has a small radius, it indicates that the elements therein exhibit a very close relationship to each other in the frequency space. A larger radius indicates less similarities between elements in the frequency space.
  • the similarity between two words may be measured using the Manhattan distance metric between their feature vectors.
  • Manhattan distance is based on the sum of the absolute value of the differences among the vector's coordinates.
  • Euclidean and maximum metrics may be used to measure distances.
  • the Manhattan distance metric was shown to provide better results than the Euclidean or maximum distance metrics.
  • a hierarchical “cluster tree” is grown representing a hierarchy of the clusters. At one node in the tree, the centroid and radius of a first cluster is stored. Two branches extend from the node to other nodes where the centroids and radii of subsumed clusters are stored. Thus, the tree structure maintains the centroid and radius of every cluster built according to Step 1030 . Step 1030 recurs until a single, all encompassing cluster encloses all clusters and input words. This cluster is termed the “root cluster” because stored as the root node of the cluster tree.
  • An exemplary cluster tree is shown in FIG. 3 .
  • the root cluster N 13 has a radius large enough to enclose all clusters and input words.
  • the root cluster therefore, possesses very little lexical significance.
  • the method 1000 cuts the cluster tree along a predetermined line in the tree structure.
  • the cutting line separates large clusters from smaller clusters.
  • the large clusters are discarded. What remains are smaller clusters, those with greater lexical significance.
  • the cutting line determines the number of clusters that will remain. One may use the median of the distances between clusters merged at the successive stages as a basis for the cutting line and prune the cluster tree at the point where cluster distances exceed this median value. Clusters are defined by the structure of the tree above the cutoff point.
  • the method 1000 ranks the remaining clusters (Step 1050 ).
  • the lexical significance of a particular cluster is measured by its compactness value.
  • the compactness value of a cluster simply may be its radius or an average distance of the members of the cluster from the centroid of the cluster.
  • the list of clusters obtained from Step 1050 is a grammatical model of the input text.
  • Adjacency of words is but one relationship that the method 1000 may be applied to recognize from a corpus. More generally, however, the method 1000 may be used to recognize predetermined relationships among tokens of the corpus. For example, the method 1000 can be configured to recognize words that appear together in the same sentences or words that appear within predetermined positional relationships with punctuation. Taken even further, the method 1000 may be configured to recognize predetermined grammatical constructs of language, such as subjects and/or objects of verbs. Each of these latter examples of relationships may require that the method be pre-configured to recognize the grammatical constructs.

Abstract

In a method of learning grammar from a corpus, context words are identified from a corpus. For the other non-context words, the method counts the occurrence of predetermined relationships which the context words, and maps the counted occurrences to a multidimensional frequency space. Clusters are grown from the frequency vectors. The clusters represent classes of words; words in the same cluster possess the same lexical significancy and provide an indicator of grammatical structure.

Description

This is a continuation of application Ser. No. 09/207,326, filed Dec. 7, 1998.
BACKGROUND
The present invention relates to an application that builds linguistic models from a corpus of speech.
For a machine to comprehend speech, not only must the machine identify spoken (or typed) words, but it also must understand language grammar to comprehend the meaning of commands. Accordingly, much research has been devoted to the construction of language models that a machine may use to ascibe meaning to spoken commands. Often, language models are preprogrammed. However, such predefined models increase the costs of a speech recognition system. Also, the language models obtained therefrom have narrow applications. Unless a programmer predefines the language model to recognize a certain command, the speech recognition system that uses the model may not recognize the command. What is needed is a training system that automatically extracts grammatical relationships from a predefined corpus of speech.
SUMMARY
An embodiment of the present invention provides a method of learning grammar from a corpus, in which context words are identified from a corpus. For the other non-context words, the method counts the occurrence of predetermined relationships with the context words, and maps the counted occurrences to a multidimensional frequency space. Clusters are grown from the frequency vectors. The clusters represent classes of words; words in the same cluster posses the same lexical significancy and provide an indicator of grammatical structure.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flow diagram of a method of an embodiment of the present invention.
FIG. 2 illustrates mapping frequency vectors that may be obtained during operation of the present invention.
FIG. 3 illustrates an exemplary cluster tree.
DETAILED DESCRIPTION
Embodiments of the present invention provide a system that automatically builds a grammatical model from a corpus of speech. The present invention uses clustering to group words and/or phrases according to their lexical significance. Relationships between high frequency words called “context words” and other input words are identified. The words to be clustered are each represented as a feature vector constructed from the identified relationships. Similarities between two input words are measured in terms of the distance between their feature vectors. Using these distances, input words are clustered according to a hierarchy. The hierarchy is then cut at a certain depth to produce clusters which are then ranked by a “goodness” metric. Those clusters that remain identify words or tokens from the corpus that possess similar grammatical significance.
Clustering per se is known. In the context of language modeling, clustering has typically been used on words to induce classes that are then used to predict smoothed probabilities of occurrence for rare or unseen events in the training corpus. Most clustering schemes use the average entropy reduction to decide when two words fall into the same cluster. Prior use of clustering, however, does not provide insight into language model of grammar.
FIG. 1 illustrates a method of the present invention according to a first embodiment. The method operates upon input text, a set of words from which the grammar model shall be constructed. Typically, the input text comprises a set of single words or phonemes. From the input text, the method identifies context words (Step 1010). Context words are those words or phonemes in the input text that occur with the highest frequency. The method 1000 may cause a predetermined number of words (say, 50) that occur with the highest frequency to be identified as context words.
The method 1000 determines relationships that may exist between the context words and the remaining words, called “input words” herein, in the input text. For example, the method 1000 may determine how many times and in which positions an input word appears adjacent to a context word. Table 1 below illustrates relationships that may exist between certain exemplary input words and exemplary context words.
TABLE 1
Context Word
to from in
Input Word −2 −1 1 2 −2 −1 1 2 −2 −1 1 2
Chicago f111 f112 f113 f114 f121 f122 f123 f124 f131 f132 f133 f134
New York f211 f212 f213 f214 f221 f222 f223 f224 f231 f232 f233 f234
Baltimore f311 f312 f313 f314 f321 f322 f323 f324 f331 f332 f333 f334
red f411 f412 f413 f414 f421 f422 f423 f424 f431 f432 f433 f434
white f511 f512 f513 f514 f521 f522 f523 f524 f531 f532 f533 f534
blue f611 f612 f613 f614 f621 f622 f623 f624 f631 f632 f633 f634
Each entry of the table, fijk represents, for a given input word i; how many times a context word C; and non-context word i; appears within a predetermined relationship. Thus, f111-F1114 each represent the number of time the input word “Chicago” and the context word “to” appear within adjacencies of −2 words, −1 word, +1 word and +2 words respectively.
Based upon the frequencies, an N dimensional vector may be built for each input word (step 1020). The number of dimensions N of the frequency vector is a multiple of the total number of context words, the total number of input words and the total number of relations identified by the method 1000. The vector represents grammatical links that exist between the input words and the context words. Thus, each input word maps to an N dimensional frequency space. A representative frequency space is shown in FIG. 2 (N=3).
The method 1000 builds clusters of input words (Step 1030). According to the principles of the present invention, input words having the same lexical significance should possess similar vectors in the frequency space. Thus, it is expected that city names will exhibit frequency characteristics that are similar to each other but different from other input words having a different lexical significance. They will be included in a cluster (say, cluster 10, FIG. 2). So, too, with colors. They will be included in another cluster (say, cluster 20). Where words exhibit similar frequency significance, they are included within a single cluster.
As is known, a cluster may be represented in an N-dimensional frequency space by a centroid coordinate and a radius indicating the volume of the cluster. The radius indicates the “compactness” of the elements within a cluster. Where a cluster has a small radius, it indicates that the elements therein exhibit a very close relationship to each other in the frequency space. A larger radius indicates less similarities between elements in the frequency space.
The similarity between two words may be measured using the Manhattan distance metric between their feature vectors. Manhattan distance is based on the sum of the absolute value of the differences among the vector's coordinates. Alternatively, Euclidean and maximum metrics may be used to measure distances. Experimentally, the Manhattan distance metric was shown to provide better results than the Euclidean or maximum distance metrics.
Step 1030 may be applied recursively to grow clusters from clusters. That is, when two clusters are located close to one another in the N dimensional space, the method 1000 may enclose them in a single cluster having its own centroid and radius. The method 1000 determines a distance between two clusters by determining the distance between their centroids using one of the metrics discussed above with respect to the vectors of input words. Thus, the Manhattan, Euclidean and maximum distance metrics may be used.
A hierarchical “cluster tree” is grown representing a hierarchy of the clusters. At one node in the tree, the centroid and radius of a first cluster is stored. Two branches extend from the node to other nodes where the centroids and radii of subsumed clusters are stored. Thus, the tree structure maintains the centroid and radius of every cluster built according to Step 1030. Step 1030 recurs until a single, all encompassing cluster encloses all clusters and input words. This cluster is termed the “root cluster” because stored as the root node of the cluster tree. An exemplary cluster tree is shown in FIG. 3.
As will be appreciated, the root cluster N13 has a radius large enough to enclose all clusters and input words. The root cluster, therefore, possesses very little lexical significance. By contrast, “leaf clusters,” those provided at the ends of branches in the cluster tree, possess very strong lexical significance.
At Step 1040, the method 1000 cuts the cluster tree along a predetermined line in the tree structure. The cutting line separates large clusters from smaller clusters. The large clusters are discarded. What remains are smaller clusters, those with greater lexical significance.
The cutting line determines the number of clusters that will remain. One may use the median of the distances between clusters merged at the successive stages as a basis for the cutting line and prune the cluster tree at the point where cluster distances exceed this median value. Clusters are defined by the structure of the tree above the cutoff point.
Finally, the method 1000 ranks the remaining clusters (Step 1050). The lexical significance of a particular cluster is measured by its compactness value. The compactness value of a cluster simply may be its radius or an average distance of the members of the cluster from the centroid of the cluster. Thus, the tighter clusters exhibiting greater lexical significance will occur first in the ranked list of clusters and those exhibiting lesser lexical significance will occur later in the list. The list of clusters obtained from Step 1050 is a grammatical model of the input text.
The method 1000 is general in that it can be used to either cluster “tokens” at any lexical level. For example, it may be applied to words and/or phrases. Table 2 illustrates the result of clustering words and Table 3 illustrates the result of clustering phrases as performed on an experimental set of training data taken from the How May I Help You? Training corpus disclosed in Gorin, et al., “How May I Help You?,” vol. 23, Speech Communication, pp. 113-127 (1997). Other lexical granularities (syllables, phonemes) also may be used.
TABLE 2
Results of Clustering Words
from AT&T's How May I Help You ? Corpus
Class Compactness
Index Value Class Members
C363 0.131 make place
C118 0.18  eight eighty five four nine oh one seven six three
two zero
C357 0.19  bill charge
C260 0.216 an and because but so when
C300 0.233 K O ok
C301 0.236 from please
C277 0.241 again here
C202 0.252 as it's
C204 0.263 different third
C77  0.268 number numbers
C275 0.272 need needed want wanted
C256 0.274 assistance directory information
C197 0.278 all before happened
C68  0.278 ninety sixty
C41  0.29  his our the their
C199 0.291 called dialed got have
C27  0.296 as by in no not now of or something that that's there
whatever working
C327 0.296 I I'm I've
C48  0.299 canada england france germany israel italy japan
london mexico paris
C69  0.308 back direct out through
C143 0.312 connected going it
C89  0.314 arizona california carolina florida georgia illinois
island jersey maryland michigan missouri ohio
pennsylvania virginia west york
C23  0.323 be either go see somebody them
C90  0.332 about me off some up you
TABLE 3
Results from a First Iteration of Combining Phrase Acquisition and Clustering
from the How May I Help You ? Corpus [Words in a Phrase are Separated by a Colon].
Class
Index Compactness Value Class Members
D365 0.226 wrong:C77 second
D325 0.232 C256:C256 C256
D380 0.239 area:code:C118:C118:C118:C118:C118 C68
D386 0.243 A:C77 this:C77
D382 0.276 C260:C357:C143:to:another
C260:C357:C143:to:my:home
D288 0.281 C327:C275:to:C363 I'd:like:toC363 to:363
yes:I'd:like:to:C363
D186 0.288 good:morning yes:ma'am yes:operator hello hi
ma'am may well
D148 0.315 problems trouble
D87  0.315 A:T:C260:T C260:C327 C27:C27 C41:C77 C118
C143 C260 C197 C199 C202 C23 C260 C27
C277 C301 C69 C77 C90 operator to
D183 0.321 C118:C118:hundred C204 telephone
D143 0.326 new:C89 C48 C89 colorado massachusetts
tennessee texas
D387 0.327 my:home my:home:phone
D4  0.336 my:calling my:calling:card my:card
D70  0.338 C199:a:wrong:C77 misdialed
D383 0.341 like:to:C363 trying:to:C363 would:like:to:C363
D381 0.347 like:to:C363:a:collect:call:to
like:to:C363:collect:call
would:like:to:C363:a:collect:call
would:like:to:C363:a:collect:call
would:like:to:C363:a:collect:call:to
D159 0.347 C118:C118 C118:C118:C118
C118:C118:C118:C118:C118:C118
C118:C118:C118:C118:C118:C118:C118
C118:C118:C118:C118:C118:C118:C118:C118:C118:C118
C:118:C118:C118:C118:C118:C118:C118:C118:C118:C118:C118
area:code:C118:C118:C118 C300
Adjacency of words is but one relationship that the method 1000 may be applied to recognize from a corpus. More generally, however, the method 1000 may be used to recognize predetermined relationships among tokens of the corpus. For example, the method 1000 can be configured to recognize words that appear together in the same sentences or words that appear within predetermined positional relationships with punctuation. Taken even further, the method 1000 may be configured to recognize predetermined grammatical constructs of language, such as subjects and/or objects of verbs. Each of these latter examples of relationships may require that the method be pre-configured to recognize the grammatical constructs.
Several embodiments of the present invention are specifically illustrated and described herein. However, it will be appreciated that modifications and variations of the present invention are covered by the above teachings and within the purview of the appended claims without departing from the spirit and intended scope of the invention.

Claims (30)

We claim:
1. A method of grammar learning from a corpus, comprising:
identifying context tokens within the corpus,
for each non-context token in the corpus, counting occurrences of a predetermined relationship of the non-context token to the context tokens,
generating frequency vectors for each non-context token based upon the counted occurrences,
clustering non-context tokens based upon the frequency vectors into a cluster, and
writing a representation of the cluster to a file,
wherein the cluster indicates a lexical correlation among the clustered non-context tokens.
2. A machine-readable medium having stored thereon executable instructions that when executed by a processor, cause the processor to:
identify context tokens within a corpus,
count occurrences of predetermined relationships of non-context tokens to the context tokens,
generating frequency vectors for each non-context token based on the counted occurrences, and
clustering the non-context tokens based upon the frequency vectors into a cluster tree.
3. The method machine-readable medium of claim 2, wherein the cluster tree represents a grammatical relationship among the non-context tokens.
4. The method machine-readable medium of claim 2, wherein the instructions further cause the processor to cut the cluster tree along a cutting line to separate large clusters from small clusters.
5. The method machine-readable medium of claim 4, wherein small clusters are ranked according to a compactness value.
6. The method machine-readable medium of claim 2, wherein the predetermined relationship is a measure of adjacency.
7. The method machine-readable medium of claim 2, wherein the clustering is performed based on Euclidean distances between the frequency vectors.
8. The method machine-readable medium of claim 2, wherein the clustering is performed based on Manhattan distances between the frequency vectors.
9. The method machine-readable medium of claim 2, wherein the clustering is performed based on maximum distance metrics between the frequency vectors.
10. The method machine-readable medium of claim 2, further comprising normalizing the frequency vectors based upon a number of occurrences of the non-context token in the corpus.
11. The method machine-readable medium of claim 2, wherein the frequency vectors are multi-dimensional vectors, the number of dimensions being determined by the number of context tokens and a number of predetermined relationships of non-context tokens to the context token being counted.
12. A grammar modeling apparatus, comprising:
means for identifying context tokens within an input corpus,
for each non-context token in the corpus, means for counting occurrences of predetermined relationships of the non-context token to a plurality of context tokens,
means for generating frequency vectors for each non-context token based upon the counted occurrences, and
means for clustering the non-context tokens into clusters based upon the frequency vectors, thereby forming a grammatical model of the corpus.
13. The apparatus of claim 12, wherein the clusters form a hierarchical cluster tree.
14. The apparatus of claim 13, wherein the hierarchical cluster tree is cut along a cutting line.
15. The apparatus of claim 12, wherein a cluster may be represented by a centroid vector.
16. The apparatus of claim 12, wherein the predetermined relationship is a measure of adjacency.
17. The apparatus of claim 12, wherein the clusters are created based on Euclidean distances between the frequency vectors.
18. The apparatus of claim 12, wherein the clusters are created based on Manhattan distances between the frequency vectors.
19. The apparatus of claim 12, wherein the clusters are created based on maximum distance metrics between the frequency vectors.
20. The apparatus of claim 12, wherein the frequency vectors are normalized based upon the number of occurrences of the non-context token in the corpus.
21. The apparatus of claim 12, wherein the frequency vectors are multi-dimensional vectors, the number of dimensions of which is determined by the number of context tokens and the number of predetermined relationships of non-context tokens to context tokens.
22. A file storing a grammar model of a corpus of speech, created according to a method comprising:
identifying context tokens within a corpus,
for each non-context token in the corpus, counting each occurrence of an identified context token having a predetermined relationship to the non-context token,
generating frequency vectors for each non-context token based upon the counted occurrences,
generating clusters of non-context tokens based upon a correlation between the frequency vectors, and storing the non-context tokens and a representation of the clusters in a file.
23. The file of claim 22, wherein the clusters may be represented by centroid vectors.
24. The file of claim 22, wherein the predetermined relationship is adjacency.
25. The file of claim 22, wherein the correlation is based on Euclidean distance.
26. The file of claim 22, the correlation is based on Manhattan distance.
27. The file of claim 22, wherein the correlation is based on a maximum distance metric.
28. The file of claim 22, wherein the frequency vectors are normalized based upon the number of occurrences of the non-context token in the corpus.
29. The file of claim 22, wherein the frequency vectors are multi-dimensional vectors, the number of dimensions of which is determined by the number of context tokens and the number of predetermined relationships of non-context tokens to context tokens.
30. The file of claim 22, wherein the file is stored on a machine-readable medium.
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